Health Care Costs Associated with AKI
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: An understanding of the health care resource use associated with AKI is needed to frame the investment and cost-effectiveness of strategies to prevent AKI and promote kidney recovery. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: . Outpatient serum creatinine measurements 6 months preceding admission defined baseline kidney function, and serum creatinine during the first 14 days of hospitalization defined Acute Kidney Injury Network stage; kidney recovery defined as serum creatinine within 25% of baseline and independence from dialysis was assessed at 90 days after AKI. Health care utilization and costs (in 2015 Canadian dollars) were determined from inpatient, outpatient, and physician claims datasets during the index hospitalization, recovery period (90 days post-AKI assessment), and 3-12 months post-AKI. A fully adjusted generalized linear model regression analysis was used to estimate costs associated with AKI. RESULTS: Of 239,906 hospitalized subjects, 25,495 (10.6%), 4598 (1.9%), 2493 (1.0%), and 670 (0.3%) had Acute Kidney Injury Network stages 1, 2, 3 without dialysis, and 3 with dialysis, respectively. Greater severity of AKI was associated with incremental increases in length of stay (+2.8; 95% confidence interval, 1.4 to 4.3 to +7.4; 95% confidence interval, 7.2 to 7.5 days) and costs (+$3779; 95% confidence interval, $3555 to $4004 to +$18,291; 95% confidence interval, $15,573 to $21,009 Canadian dollars) from admission to recovery assessment (3 months). At months 3-12 postadmission, compared with subjects without AKI, AKI with kidney recovery and AKI without kidney recovery were associated with incremental costs of +$2912-$3231 and +$6035-$8563 Canadian dollars, respectively. The estimated incremental cost of AKI in Canada is estimated to be over $200 million Canadian dollars per year. CONCLUSIONS: Severity of AKI, need for dialysis, and lack of kidney recovery are associated with significant health care costs in hospitalized patients and persist a year after admission. Strategies to identify, prevent, and facilitate kidney recovery are needed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".